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Event‐specific data envelopment models and efficiency analysis

Listed author(s):
  • Robert G. Chambers
  • Atakelty Hailu
  • John Quiggin

Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state-contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event-specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event-specific DEA representation, we apply it to a data set for Western Australian wheat farmers. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.

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Article provided by Australian Agricultural and Resource Economics Society in its journal Australian Journal of Agricultural and Resource Economics.

Volume (Year): 55 (2011)
Issue (Month): 1 (January)
Pages: 90-106

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Handle: RePEc:bla:ajarec:v:55:y:2011:i:1:p:90-106
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  1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
  2. Fraser, Iain & Graham, Mary, 2005. "Efficiency Measurement of Australian Dairy Farms: National and Regional Performance," Australasian Agribusiness Review, University of Melbourne, Melbourne School of Land and Environment, vol. 13.
  3. John Quiggin & David Adamson & Sarah Chambers & Peggy Schrobback, 2010. "Climate Change, Uncertainty, and Adaptation: The Case of Irrigated Agriculture in the Murray-Darling Basin in Australia," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 58(s1), pages 531-554, December.
  4. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521622448, December.
  5. Robert G. Chambers & Erik Lichtenberg, 1996. "A Nonparametric Approach to the von Liebig-Paris Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 373-386.
  6. Banker, Rajiv D. & Chang, Hsihui, 1995. "A simulation study of hypothesis tests for differences in efficiencies," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 37-54, April.
  7. Chambers, Christopher P. & Miller, Alan D., "undated". "Inefficiency," Working Papers WP2011/14, University of Haifa, Department of Economics, revised 30 Nov 2011.
  8. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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